To compare representational power of SSL models for articulations, we assess articulatory score for SSL models. Highest scores are chosen among the multiple layers, and these scores are reported on Fig. REF . The overall pattern is consistent with SUPERB [1]}, where Transformer based models trained on contrastive or predictive objectives (e.g., Wav2Vec 2.0, HuBERT, WavLM) are generally better than the other.
The ranks of the models mostly match those from the SUPERB leaderboard (Spearman's rank correlation = 0.93).0.86 on more inclusive model space (20 models). Individual scores are omitted due to space limitation. One unexpected deviation is Data2Vec Large, which shows lower score than the score of HuBERT Base.
